Knowledge Transfer Between Computer Vision And Text Mining: Similarity-based Learning Approaches by Radu Tudor IonescuKnowledge Transfer Between Computer Vision And Text Mining: Similarity-based Learning Approaches by Radu Tudor Ionescu

Knowledge Transfer Between Computer Vision And Text Mining: Similarity-based Learning Approaches

byRadu Tudor Ionescu, Marius Popescu

Hardcover | May 9, 2016

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This ground-breaking text/reference divergesfrom the traditional view that computer vision (for image analysis) and stringprocessing (for text mining) are separate and unrelated fields of study,propounding that images and text can be treated in a similar manner for thepurposes of information retrieval, extraction and classification. Highlightingthe benefits of knowledge transfer between the two disciplines, the textpresents a range of novel similarity-based learning (SBL) techniques founded onthis approach. Topics and features: describes a variety of SBL approaches,including nearest neighbor models, local learning, kernel methods, andclustering algorithms; presents a nearest neighbor model based on a noveldissimilarity for images; discusses a novel kernel for (visual) wordhistograms, as well as several kernels based on a pyramid representation; introducesan approach based on string kernels for native language identification; containslinks for downloading relevant open source code.
Dr. Radu Tudor Ionescu is an Assistant Professor in the Department of Computer Science at the University of Bucharest, Romania.Dr. Marius Popescu is an Associate Professor at the same institution.
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Title:Knowledge Transfer Between Computer Vision And Text Mining: Similarity-based Learning ApproachesFormat:HardcoverDimensions:250 pages, 23.5 × 15.5 × 0.25 inPublished:May 9, 2016Publisher:Springer-Verlag/Sci-Tech/TradeLanguage:English

The following ISBNs are associated with this title:

ISBN - 10:3319303651

ISBN - 13:9783319303659

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Table of Contents

Motivation and Overview

Learning Based on Similarity

Part I: Knowledge Transfer from Text Mining to Computer Vision

State of the Art Approaches for Image Classification

Local Displacement Estimation of Image Patches and Textons

Object Recognition with the Bag of Visual Words Model

Part II: Knowledge Transfer from Computer Vision to Text Mining

State of the Art Approaches for String and Text Analysis

Local Rank Distance

Native Language Identification with String Kernels

Spatial Information in Text Categorization

Conclusions